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All Journal JSI: Jurnal Sistem Informasi (E-Journal) bit-Tech
Alrafiful Rahman
Perbanas Institute
Computer Science & IT Decision Sciences, Operations Research & Management Library & Information Science Other

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Comparative Analysis of K-Means and Gaussian Mixture Model in Clustering Global CO2 Emissions Valentinus Paramarta; Alrafiful Rahman; Lely Priska; Rizon Roken Gurning; Widya Ayu Purwati
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2805

Abstract

As global CO2 emissions continue to rise, identifying meaningful patterns across countries has become increasingly vital for shaping effective climate policies. However, many existing approaches rely on uniform benchmarks that overlook national emission heterogeneity. To address this gap, this study applies two unsupervised machine learning techniques K-Means and Gaussian Mixture Model (GMM) to cluster countries based on CO2 emissions from both energy and industrial sectors. The dataset consists of six key indicators, including total emissions, growth rate, and sectoral shares. After handling missing values and applying Min-Max normalization, Principal Component Analysis (PCA) was used to reduce dimensionality and aid visualization. The core objective is to compare the effectiveness of K-Means and GMM in identifying emission-based country groupings. K-Means produced three distinct clusters with strong separation, including a unique cluster dominated solely by China due to its exceptional emission profile. GMM, by contrast, generated more flexible probabilistic clusters, better capturing overlapping patterns and internal variabilities among countries. Evaluation metrics showed that K-Means outperformed GMM in silhouette score and inertia, indicating clearer boundaries, while GMM was more adept at modeling complex, non-spherical distributions. These findings reveal the trade-offs between clarity and adaptability in clustering approaches. The study demonstrates how unsupervised learning can offer actionable insights for emission-based segmentation, enabling more nuanced and differentiated mitigation strategies. By highlighting algorithm-specific strengths, this research contributes to the advancement of machine learning applications in climate informatics and supports the development of targeted international environmental responses.
Evaluasi Kinerja Platform Pengelolaan Layanan Teknologi Informasi (ITSM) Menggunakan Metode COBIT 5.0 Verayani Fronika Simbolon; Mardiana Purwaningsih; Alrafiful Rahman
JSI: Jurnal Sistem Informasi (E-Journal) Vol 18 No 2 (2026): JSI: Jurnal Sistem Informasi (E-Journal)
Publisher : Jurusan Sistem Informasi Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/jsi.v18i2.545

Abstract

Kegagalan dalam pengelolaan insiden dapat meningkatkan risiko keamanan informasi dan ketidakpatuhan terhadap regulasi seperti GDPR atau ISO 27001, serta berdampak pada kepuasan pelanggan dan rekan bisnis. Untuk mengantisipasi kegagalan aplikasi maka perlu dilakukan evaluasi kinerja aplikasi tersebut. Penelitian ini bertujuan mengidentifikasi masalah dalam pemantauan dan evaluasi kontrol internal serta memastikan aplikasi Ivanti memenuhi persyaratan eksternal. Berdasarkan hasil perhitungan, evaluasi maturitas yang dilakukan pada subdomain MEA (Monitor, Evaluate, and Assess) dengan skor MEA 01 (3,496), MEA 02 (3,912), dan MEA 03 (4,034), menunjukkan tingkat maturitas ada pada level "Defined" dan "Managed and Measurable". Hasil ini menunjukkan bahwa Ivanti memiliki kerangka kerja terdokumentasi untuk memantau kinerja dan kepatuhan, namun masih memerlukan perbaikan dalam transfer tiket antar tim, kontrol internal, dan pelatihan pengguna. Temuan di subdomain MEA 01 menunjukkan bahwa aplikasi Ivanti belum optimal dalam transfer tiket antara tim IT dan non-IT serta penanganan insiden, menyebabkan inefisiensi operasional dan penurunan kualitas layanan IT. Temuan di subdomain MEA 02 menunjukkan bahwa sistem kontrol internal di PT. ABC Finance masih perlu perbaikan untuk menghindari duplikasi kerja dan memastikan tiket ditangani dengan tepat. Evaluasi menggunakan framework COBIT 5.0 menekankan pentingnya dokumentasi dan pengukuran proses TI untuk mengelola risiko dan memastikan efektivitas operasional untuk mendukung penelitian lebih lanjut.
Co-Authors Lely Priska Mardiana Purwaningsih Rizon Roken Gurning Valentinus Paramarta Verayani Fronika Simbolon Widya Ayu Purwati